{"ID":2841481,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17566","arxiv_id":"2511.17566","title":"Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs","abstract":"Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI). Despite substantial research efforts, conventional diagnostic approaches still face two key challenges. First, these methods predominantly adopt a joint learning paradigm for RCL and FTI to exploit shared information and reduce training time. However, this simplistic integration neglects the causal dependencies between tasks, thereby impeding inter-task collaboration and information transfer. Second, these existing methods primarily focus on point-to-point relationships between instances, overlooking the group nature of inter-instance influences induced by deployment configurations and load balancing. To overcome these limitations, we propose CCLH, a novel root cause analysis framework that orchestrates diagnostic tasks based on cascaded conditional learning. CCLH provides a three-level taxonomy for group influences between instances and incorporates a heterogeneous hypergraph to model these relationships, facilitating the simulation of failure propagation. Extensive experiments conducted on datasets from three microservice benchmarks demonstrate that CCLH outperforms state-of-the-art methods in both RCL and FTI.","short_abstract":"Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI). Despite substantial research efforts, conventional diagnostic approaches still face two key challenges. First, these methods predominantly adopt a joint learning paradigm f...","url_abs":"https://arxiv.org/abs/2511.17566","url_pdf":"https://arxiv.org/pdf/2511.17566v1","authors":"[\"Shuaiyu Xie\",\"Hanbin He\",\"Jian Wang\",\"Bing Li\"]","published":"2025-11-14T03:20:02Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DC\",\"cs.SE\"]","methods":"[]","has_code":false}